The availability of complete genome sequences is ushering in a new era in the analysis of gene regulation. For example, the full genome sequence of the yeast Saccharomyces cerevisiae is ideal for such studies. Many yeast biochemical and genetic pathways are well understood, and its genome contains a manageable number (6000) of genes which are arranged in a compact fashion. Previously, we have studied the sequences of all potential yeast promoters in order to identify genes whose transcription is co-regulated during the yeast cell cycle. Such work may result in a better understanding of the complex regulatory networks that orchestrate correct quantitative and temporal patterns of gene expression. In a newer experiment with yeast, we have identified biological features associated with double stranded break frequencies during meiotic recombination. Using a feature selection approach, we identified five features that distinguish hot from cold recombination hotspots in Saccharomyces cerevisiae with high accuracy. These are the histone marks H3K4me3, H3K14ac, H3K36me3, and H3K79me3; and GC content. We have also addressed the role that histone modifications play in chromatin organization in yeast. We have shown that there are strong positional preferences for sequence-specific chromatin modifying protein-binding motifs in potential regulatory regions. We have used DNA-binding motifs recognition algorithms and gene ontology enrichment tools to make these discoveries. We continue to interrogate many datasets to establish common properties of chromatin during a variety of active states. In a new collaboration, we are analyzing the mouse genome using new analytical tools and statistics to compare the results of several next generation sequencing (NGS) experiments. Data from ChIPseq, microarray and RNAseq experiments were included for analysis in order to further assess the role of HMGN1 and HMGN2 proteins in chromatin organization and gene expression. We developed analysis pipelines for ChIP-seq experiments of DNA sequences bound to HMGN1, and HMGN2 in wildtype and knockout mice. The outcome of these collaborations is that we have developed and efficient and adjustable pipeline for the analysis of many NGS datasets in a reasonable time and can easily interrogate the data to further develop biological interpretations.